vavilovВавиловский журнал генетики и селекцииVavilov Journal of Genetics and Breeding2500-3259Institute of Cytology and Genetics of Siberian Branch of the RAS10.18699/VJGB-23-95vavilov-3982Research ArticleЭВОЛЮЦИОННАЯ КОМПЬЮТЕРНАЯ БИОЛОГИЯEVOLUTIONARY COMPUTATIONAL BIOLOGYПоиск дифференциально метилированных регионов в геномах древних и современных людейSearch for differentially methylated regions in ancient and modern genomeshttps://orcid.org/0000-0003-3596-5470БородкоД. Д.BorodkoD. D.

Москва

Moscow

daria.borodko@gmail.comhttps://orcid.org/0000-0003-0874-1594ЖенилоС. В.ZheniloS. V.

Москва

Moscow

https://orcid.org/0000-0002-1189-5597ШаркоФ. С.SharkoF. S.

Москва

Moscow

Федеральный исследовательский центр «Фундаментальные основы биотехнологии» Российской академии наукРоссияFederal Research Center “Fundamentals of Biotechnology” of the Russian Academy of SciencesRussian Federation202311122023277820­-828Copyright © Бородко Д.Д., Женило С.В., Шарко Ф.С., 20232023Бородко Д.Д., Женило С.В., Шарко Ф.С.Borodko D.D., Zhenilo S.V., Sharko F.S.This work is licensed under a Creative Commons Attribution 4.0 License.https://vavilov.elpub.ru/jour/article/view/3982

В настоящее время активно исследуются механизмы, регулирующие развитие различных патологий и их эволюционную динамику. Эпигенетические механизмы, такие как метилирование, играют значимую роль в эволюционных процессах, поскольку их изменения гораздо быстрее отражаются на фенотипе, чем результаты мутагенеза. В данном исследовании мы предприняли попытку разработать алгоритм для выявления дифференциально метилированных областей, связанных с метаболическим синдромом, которые изменили свое метилирование у человека при переходе от охоты и собирательства к оседлой жизни. Применение существующих методов полногеномного бисульфитного секвенирования ограничено для древних образцов из­за их низкого качества и фрагментации, и подход к получению профилей метилирования охотников­собирателей значительно отличается от подходов, используемых для современных тканей. В этой работе мы валидировали DamMet – алгоритм, реконструирующий древние метиломы. Применение DamMet к геномам неандертальца и денисовца показало средний уровень корреляции с профилями метилирования, опубликованными ранее, а также продемонстрировало занижение уровня метилирования реконструированных профилей в среднем на 15–20 %. Также мы разработали новый алгоритм на языке Python, позволяющий сравнивать метиломы в древних и современных образцах, не смотря на отсутствие профилей метилирования современных образцов костной ткани в контексте ожирения. Такой анализ подразумевает двухступенчатую обработку данных, где на первом этапе происходит идентификация тканеспецифичных областей метилирования и их фильтрация, а на втором этапе осуществляется непосредственно поиск дифференциально метилированных регионов в заданных областях, ассоциированных с интересую щим исследователя заболеванием. В результате использования алгоритма на тестовых данных мы обнаружили 38 дифференциально метилированных регионов, ассоциированных с ожирением, большая часть которых принадлежала промоторным областям, и разработанный пайплайн показал достаточную эффективность в их поиске. Эти результаты подтверждают возможность восстановления профилей метилирования в древних образцах и их сравнения с современными метиломами. Также обсуждаются возможности дальнейшего развития методологии и внедрения нового шага, позволяющего изучать дифференциально метилированные позиции, связанные с эволюционными процессами. 

Currently, active research is focused on investigating the mechanisms that regulate the development of various pathologies and their evolutionary dynamics. Epigenetic mechanisms, such as DNA methylation, play a significant role in evolutionary processes, as their changes have a faster impact on the phenotype compared to mutagenesis. In this study, we attempted to develop an algorithm for identifying differentially methylated regions associated with metabolic syndrome, which have undergone methylation changes in humans during the transition from a hunter­gatherer to a sedentary lifestyle. The application of existing whole­genome bisulfite sequencing methods is limited for ancient samples due to their low quality and fragmentation, and the approach to obtaining DNA methylation profiles differs significantly between ancient hunter­gatherer samples and modern tissues. In this study, we validated DamMet, an algorithm for reconstructing ancient methylomes. Application of DamMet to Neanderthal and Denisovan genomes showed a moderate level of correlation with previously published methylation profiles and demonstrated an underestimation of methylation levels in the reconstructed profiles by an average of 15–20 %. Additionally, we developed a new Python­based algorithm that allows for the comparison of methylomes in ancient and modern samples, despite the absence of methylation profiles in modern bone tissue within the context of obesity. This analysis involves a two­step data processing approach, where the first step involves the identification and  filtration of tissue­specific methylation regions, and the second step focuses on the direct search for differentially methylated regions in specific areas associated with the researcher’s target condition. By applying this algorithm to test data, we identified 38 differentially methylated regions associated with obesity, the majority of which were located in promoter regions. The pipeline demonstrated sufficient efficiency in detecting these regions. These results confirm the feasibility of reconstructing DNA methylation profiles in ancient samples and comparing them with modern methylomes. Furthermore, possibilities for further methodological development and the implementation of a new step for studying differentially methylated positions associated with evolutionary processes are discussed.

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The authors declare that there are no conflicts of interest present.